Economic and financial modeling and planning is commonly used to estimate or predict the performance and outcome of real systems, given specific sets of input data of interest. An economic-based system will have many variables and influences which determine its behavior. A model is a mathematical expression or representation which predicts the outcome or behavior of the system under a variety of conditions. In one sense, it is relatively easy to review historical data, understand its past performance, and state with relative certainty that the system's past behavior was indeed driven by the historical data. A much more difficult task is to generate a mathematical model of the system which predicts how the system will behave with different sets of data and assumptions.
In its basic form, the economic model can be viewed as a predicted or anticipated outcome of a mathematical expression, as driven by a given set of input data and assumptions. The input data is processed through the mathematical expression representing either the expected or current behavior of the real system. The mathematical expression is formulated or derived from principles of probability and statistics, often by analyzing historical data and corresponding known outcomes, to achieve a best fit of the expected behavior of the system to other sets of data. In other words, the model should be able to predict the outcome or response of the system to a specific set of data being considered or proposed, within a level of confidence, or an acceptable level of uncertainty.
Economic modeling has many uses and applications. One area in which modeling has been applied is in the retail environment. Grocery stores, general merchandise stores, specialty shops, and other retail outlets face stiff competition for limited customers and business. Most if not all retail stores expend great effort to maximize sales, volume, revenue, and/or profit. Economic modeling can be a very effective tool in helping store owners and managers achieve these goals.
Economic modeling typically requires large amounts of data. In the retail environment, the data is collected at the completion of the transaction, usually during the check-out process. The transactional log (T-LOG) data contains information and attributes about the items purchased, time and date of purchase, store, price, promotions, customer attributes, and so on. The T-LOG data is stored in a database for use by the retailer in generating and using the model.
To prepare a model of sales or demand of any particular product of any particular store, the T-LOG data is first aggregated over a time scale for one or more variables to develop a unique understanding of demand for every product in every location. The aggregated T-LOG data is analyzed and a series of parameters is generated that define the demand model for that particular product over the time scale of interest. For products that have a relatively high sales volume, the T-LOG data contains many data points and the demand model for that product is relatively robust. The demand model is leveraged to produce accurate forecasts of future demand—even in the face of anticipated future price and promotional changes. The model can also be leveraged to optimize prices and support other business decisions. Multiple retail applications can be built on top of the demand model forecast in order to share its understanding of demand in a way that ensures consistent business decisions from planning through replenishment.
The store management is typically most interested in model behavior and even becomes dependent on the model when making business decisions. The performance of the model, i.e., time and resources needed to execute the model, is also important to management as they often require timely and accurate forecasts of the demand data. Management is always interested in the current model predictions in small windows of time, based on the latest T-LOG data. Modeling on a weekly basis helps improve the performance of the demand model compared to modeling on a daily basis. Aggregation to the week improves performance but limits the resolution and the utility of the model since the model cannot be directly used to forecast sales for a particular day. For example, the model behavior for product sales on Monday or Tuesday may be different than the model for product sales on Friday or Saturday. People buy more beer and chips on the weekend than during weekdays. Accordingly, management may run the model for each day of the week to get the desired resolution or granularity and be assured of operating with the best information.
The model requires significant computing resources to process potentially terabytes of T-LOG data. The output of the model also requires large data storage capacity. Running the model for each day of the week, for each and every week of the month, is time consuming, resource intensive, and costly.